1,962 research outputs found
Peer-to-peer and community-based markets: A comprehensive review
The advent of more proactive consumers, the so-called "prosumers", with
production and storage capabilities, is empowering the consumers and bringing
new opportunities and challenges to the operation of power systems in a market
environment. Recently, a novel proposal for the design and operation of
electricity markets has emerged: these so-called peer-to-peer (P2P) electricity
markets conceptually allow the prosumers to directly share their electrical
energy and investment. Such P2P markets rely on a consumer-centric and
bottom-up perspective by giving the opportunity to consumers to freely choose
the way they are to source their electric energy. A community can also be
formed by prosumers who want to collaborate, or in terms of operational energy
management. This paper contributes with an overview of these new P2P markets
that starts with the motivation, challenges, market designs moving to the
potential future developments in this field, providing recommendations while
considering a test-case
Robust Optimal Power Flow with Wind Integration Using Conditional Value-at-Risk
Integrating renewable energy into the power grid requires intelligent
risk-aware dispatch accounting for the stochastic availability of renewables.
Toward achieving this goal, a robust DC optimal flow problem is developed in
the present paper for power systems with a high penetration of wind energy. The
optimal dispatch is obtained as the solution to a convex program with a
suitable regularizer, which is able to mitigate the potentially high risk of
inadequate wind power. The regularizer is constructed based on the energy
transaction cost using conditional value-at-risk (CVaR). Bypassing the
prohibitive high-dimensional integral, the distribution-free sample average
approximation method is efficiently utilized for solving the resulting
optimization problem. Case studies are reported to corroborate the efficacy of
the novel model and approach tested on the IEEE 30-bus benchmark system with
real operation data from seven wind farms.Comment: To Appear in Proc. of the 4th Intl. Conf. on Smart Grid
Communication
On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid
In this paper, a performance comparison among three well-known stochastic model
predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained
model predictive control is presented. To this end, three predictive controllers have
been designed and implemented in a real renewable-hydrogen-based microgrid. The
experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel
cell as main equipment. The real experimental results show significant differences from
the plant components, mainly in terms of use of energy, for each implemented technique.
Effectiveness, performance, advantages, and disadvantages of these techniques
are extensively discussed and analyzed to give some valid criteria when selecting an
appropriate stochastic predictive controller.Ministerio de EconomÃa y Competitividad DPI2013-46912-C2-1-RMinisterio de EconomÃa y Competitividad DPI2013-482443-C2-1-
Risk-Aware Stochastic Scheduling of Hybrid Integrated Energy Systems with 100% Renewables
Recently, ambitious endeavors have been carried out to facilitate the transition from traditional grids to hybrid interconnected energy networks in the form of grid modernization. Align to such efforts, this article aims at developing a novel framework for satisfying techno-economic-environmental goals in the grid modernization process. To this end, a detailed examination is conducted for the optimal exploitation of energy hubs (EHs) equipped with 100% renewables to pursue the environmental goal alongside intending technical and economic constraints. The energy conversion technology is adopted to enable the power-to-gas system for establishing multi-energy interactions among electricity and gas networks. Fully benefiting from renewable units has exposed the system to uncertain fluctuations that necessitate the modeling of uncertainties to achieve near-reality results. Hence, risk-averse and seeker strategies are developed based on robustness and opportunistic modes of the information gap decision theory (IGDT) method to deal with stochastic fluctuations of uncertain parameters. The integrated electricity and gas test system is considered to analyze the applicability of the proposed framework in modeling efficient multi-energy interactions. Given the obtained results, 43.68% more energy cost is reached for EHs when they adopted a robust strategy against uncertainties under the risk-averse strategy. Moreover, the proposed framework procured a rational decision-making model for balancing multi-energy in the hybrid energy grid with 100% renewables
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